3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset

TL;DR

This paper introduces a new 3D zebrafish tracking dataset and a baseline system, addressing challenges of visual similarity and occlusion in behavioral analysis for neuroscience research.

Contribution

It provides a publicly available stereo 3D RGB dataset for zebrafish tracking and a modular baseline system with performance benchmarks.

Findings

Baseline system achieves up to 77.6% MOTA.

Dataset contains 86,400 annotated points and bounding boxes.

Eight sequences with 1-10 zebrafish each.

Abstract

In this work we present a novel publicly available stereo based 3D RGB dataset for multi-object zebrafish tracking, called 3D-ZeF. Zebrafish is an increasingly popular model organism used for studying neurological disorders, drug addiction, and more. Behavioral analysis is often a critical part of such research. However, visual similarity, occlusion, and erratic movement of the zebrafish makes robust 3D tracking a challenging and unsolved problem. The proposed dataset consists of eight sequences with a duration between 15-120 seconds and 1-10 free moving zebrafish. The videos have been annotated with a total of 86,400 points and bounding boxes. Furthermore, we present a complexity score and a novel open-source modular baseline system for 3D tracking of zebrafish. The performance of the system is measured with respect to two detectors: a naive approach and a Faster R-CNN based fish head detector. The system reaches a MOTA of up to 77.6%. Links to the code and dataset is available at the project page https://vap.aau.dk/3d-zef